69 software-defined-network-postdoc Postdoctoral positions at MOHAMMED VI POLYTECHNIC UNIVERSITY in Morocco
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) Mining initiative to achieve net-zero Scope 1 emissions, aligning with the company’s roadmap for sustainable mining operations. Key Responsibilities Literature Survey & Case Definition Systematically
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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: Center for African Studies Université / University: Mohammed VI Polytechnic University Intitulé de l’offre Postdoc / Title of the Postdoc offer : L’Afrique dans l’œuvre de Mokhtar Soussi Summary
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: Center for African Studies Université / University: Mohammed VI Polytechnic University Intitulé de l’offre Postdoc / Title of the Postdoc offer : African Anthropology: Environment and Health Summary
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conferences and journals. Overview: The successful candidate will join an interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific
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Mining Institute (GSMI) of the University Mohammed VI Polytechnic (UM6P) invites applications for a 24-month postdoc position in geochemistry with a particular emphasis on the Upper Cretaceous-Paleogene
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. Oriented towards Africa, CAES acts in connection with a wide network of universities and research centers around the continent in order to link real field issues with up-to-date science. Research on plant
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. Application materials: CV, cover letter, and one recommendation letter. Postdoc Supervisor: Pr. Mostapha TARFAOUI (GEP/GSMI), Postdoc Co-Supervisor: Dr. Abdelmalek TOUMI (ENSTA, STIC, France), Dr. Ayoub Karine
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, and TCLP to assess reprocessing and environmental remediation potential. Develop and interpret 3D block models using advanced geological modeling tools such as Datamine software, Leapfrog Geo and Surfer
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical